OpenDrugAI · suleymanov · Sep 14, 2019 · Oct 4, 2019
diff --git a/code/AttentiveFP/AttentiveLayers.py → AttentiveFP/AttentiveLayers.py b/code/AttentiveFP/AttentiveLayers.py → AttentiveFP/AttentiveLayers.py
@@ -47,28 +47,23 @@ def forward(self, atom_list, bond_list, atom_degree_list, bond_degree_list, atom
         attend_mask = atom_degree_list.clone()
         attend_mask[attend_mask != mol_length-1] = 1
         attend_mask[attend_mask == mol_length-1] = 0
-        attend_mask = attend_mask.type(torch.cuda.FloatTensor).unsqueeze(-1)
+        attend_mask = self._cast_float(attend_mask).unsqueeze(-1)
 
         softmax_mask = atom_degree_list.clone()
         softmax_mask[softmax_mask != mol_length-1] = 0
         softmax_mask[softmax_mask == mol_length-1] = -9e8 # make the softmax value extremly small
-        softmax_mask = softmax_mask.type(torch.cuda.FloatTensor).unsqueeze(-1)
+        softmax_mask = self._cast_float(softmax_mask).unsqueeze(-1)
 
         batch_size, mol_length, max_neighbor_num, fingerprint_dim = neighbor_feature.shape
         atom_feature_expand = atom_feature.unsqueeze(-2).expand(batch_size, mol_length, max_neighbor_num, fingerprint_dim)
         feature_align = torch.cat([atom_feature_expand, neighbor_feature],dim=-1)
 
         align_score = F.leaky_relu(self.align[0](self.dropout(feature_align)))
-#             print(attention_weight)
         align_score = align_score + softmax_mask
         attention_weight = F.softmax(align_score,-2)
-#             print(attention_weight)
         attention_weight = attention_weight * attend_mask
-#         print(attention_weight)
         neighbor_feature_transform = self.attend[0](self.dropout(neighbor_feature))
-#             print(features_neighbor_transform.shape)
         context = torch.sum(torch.mul(attention_weight,neighbor_feature_transform),-2)
-#             print(context.shape)
         context = F.elu(context)
         context_reshape = context.view(batch_size*mol_length, fingerprint_dim)
         atom_feature_reshape = atom_feature.view(batch_size*mol_length, fingerprint_dim)
@@ -89,16 +84,11 @@ def forward(self, atom_list, bond_list, atom_degree_list, bond_degree_list, atom
             feature_align = torch.cat([atom_feature_expand, neighbor_feature],dim=-1)
 
             align_score = F.leaky_relu(self.align[d+1](self.dropout(feature_align)))
-    #             print(attention_weight)
             align_score = align_score + softmax_mask
             attention_weight = F.softmax(align_score,-2)
-#             print(attention_weight)
             attention_weight = attention_weight * attend_mask
-#             print(attention_weight)
             neighbor_feature_transform = self.attend[d+1](self.dropout(neighbor_feature))
-    #             print(features_neighbor_transform.shape)
             context = torch.sum(torch.mul(attention_weight,neighbor_feature_transform),-2)
-    #             print(context.shape)
             context = F.elu(context)
             context_reshape = context.view(batch_size*mol_length, fingerprint_dim)
 #             atom_feature_reshape = atom_feature.view(batch_size*mol_length, fingerprint_dim)
@@ -116,7 +106,7 @@ def forward(self, atom_list, bond_list, atom_degree_list, bond_degree_list, atom
         mol_softmax_mask = atom_mask.clone()
         mol_softmax_mask[mol_softmax_mask == 0] = -9e8
         mol_softmax_mask[mol_softmax_mask == 1] = 0
-        mol_softmax_mask = mol_softmax_mask.type(torch.cuda.FloatTensor)
+        mol_softmax_mask = self._cast_float(mol_softmax_mask)
 
         for t in range(self.T):
 
@@ -126,18 +116,17 @@ def forward(self, atom_list, bond_list, atom_degree_list, bond_degree_list, atom
             mol_align_score = mol_align_score + mol_softmax_mask
             mol_attention_weight = F.softmax(mol_align_score,-2)
             mol_attention_weight = mol_attention_weight * atom_mask
-#             print(mol_attention_weight.shape,mol_attention_weight)
             activated_features_transform = self.mol_attend(self.dropout(activated_features))
-#             aggregate embeddings of atoms in a molecule
             mol_context = torch.sum(torch.mul(mol_attention_weight,activated_features_transform),-2)
-#             print(mol_context.shape,mol_context)
             mol_context = F.elu(mol_context)
             mol_feature = self.mol_GRUCell(mol_context, mol_feature)
-#             print(mol_feature.shape,mol_feature)
 
             # do nonlinearity
             activated_features_mol = F.relu(mol_feature)           
 
         mol_prediction = self.output(self.dropout(mol_feature))
 
-        return atom_feature, mol_prediction
+        return atom_feature, mol_prediction
+
+    def _cast_float(self, x):
+        return x.type(torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor)
diff --git a/code/AttentiveFP/AttentiveLayers_viz.py → AttentiveFP/AttentiveLayers_viz.py b/code/AttentiveFP/AttentiveLayers_viz.py → AttentiveFP/AttentiveLayers_viz.py
diff --git a/code/AttentiveFP/Featurizer.py → AttentiveFP/Featurizer.py b/code/AttentiveFP/Featurizer.py → AttentiveFP/Featurizer.py
diff --git a/.../AttentiveFP/Featurizer_aromaticity_rm.py → AttentiveFP/Featurizer_aromaticity_rm.py b/.../AttentiveFP/Featurizer_aromaticity_rm.py → AttentiveFP/Featurizer_aromaticity_rm.py
diff --git a/code/AttentiveFP/__init__.py → AttentiveFP/__init__.py b/code/AttentiveFP/__init__.py → AttentiveFP/__init__.py
diff --git a/code/AttentiveFP/getFeatures.py → AttentiveFP/getFeatures.py b/code/AttentiveFP/getFeatures.py → AttentiveFP/getFeatures.py
diff --git a/...AttentiveFP/getFeatures_aromaticity_rm.py → AttentiveFP/getFeatures_aromaticity_rm.py b/...AttentiveFP/getFeatures_aromaticity_rm.py → AttentiveFP/getFeatures_aromaticity_rm.py
diff --git a/code/1_POC_test_Malaria_Bioactivity.ipynb → ...ooks/1_POC_test_Malaria_Bioactivity.ipynb b/code/1_POC_test_Malaria_Bioactivity.ipynb → ...ooks/1_POC_test_Malaria_Bioactivity.ipynb
diff --git a/.../1_POC_test_Photovoltaic_efficiency.ipynb → .../1_POC_test_Photovoltaic_efficiency.ipynb b/.../1_POC_test_Photovoltaic_efficiency.ipynb → .../1_POC_test_Photovoltaic_efficiency.ipynb
diff --git a/code/1_POC_test_Solubility.ipynb → notebooks/1_POC_test_Solubility.ipynb b/code/1_POC_test_Solubility.ipynb → notebooks/1_POC_test_Solubility.ipynb
diff --git a/code/2_Bioactivity_BACE.ipynb → notebooks/2_Bioactivity_BACE.ipynb b/code/2_Bioactivity_BACE.ipynb → notebooks/2_Bioactivity_BACE.ipynb
diff --git a/code/2_Bioactivity_HIV.ipynb → notebooks/2_Bioactivity_HIV.ipynb b/code/2_Bioactivity_HIV.ipynb → notebooks/2_Bioactivity_HIV.ipynb
diff --git a/code/2_Bioactivity_MUV.ipynb → notebooks/2_Bioactivity_MUV.ipynb b/code/2_Bioactivity_MUV.ipynb → notebooks/2_Bioactivity_MUV.ipynb
diff --git a/code/2_Physical_Chemistry_ESOL.ipynb → notebooks/2_Physical_Chemistry_ESOL.ipynb b/code/2_Physical_Chemistry_ESOL.ipynb → notebooks/2_Physical_Chemistry_ESOL.ipynb
diff --git a/code/2_Physical_Chemistry_FreeSolv.ipynb → ...books/2_Physical_Chemistry_FreeSolv.ipynb b/code/2_Physical_Chemistry_FreeSolv.ipynb → ...books/2_Physical_Chemistry_FreeSolv.ipynb
diff --git a/code/2_Physical_Chemistry_Lipop.ipynb → notebooks/2_Physical_Chemistry_Lipop.ipynb b/code/2_Physical_Chemistry_Lipop.ipynb → notebooks/2_Physical_Chemistry_Lipop.ipynb
diff --git a/code/2_Physiology_or_Toxicity_BBBP.ipynb → ...books/2_Physiology_or_Toxicity_BBBP.ipynb b/code/2_Physiology_or_Toxicity_BBBP.ipynb → ...books/2_Physiology_or_Toxicity_BBBP.ipynb
diff --git a/code/2_Physiology_or_Toxicity_ClinTox.ipynb → ...ks/2_Physiology_or_Toxicity_ClinTox.ipynb b/code/2_Physiology_or_Toxicity_ClinTox.ipynb → ...ks/2_Physiology_or_Toxicity_ClinTox.ipynb
diff --git a/code/2_Physiology_or_Toxicity_SIDER.ipynb → ...ooks/2_Physiology_or_Toxicity_SIDER.ipynb b/code/2_Physiology_or_Toxicity_SIDER.ipynb → ...ooks/2_Physiology_or_Toxicity_SIDER.ipynb
diff --git a/code/2_Physiology_or_Toxicity_Tox21.ipynb → ...ooks/2_Physiology_or_Toxicity_Tox21.ipynb b/code/2_Physiology_or_Toxicity_Tox21.ipynb → ...ooks/2_Physiology_or_Toxicity_Tox21.ipynb
diff --git a/code/2_Physiology_or_Toxicity_ToxCast.ipynb → ...ks/2_Physiology_or_Toxicity_ToxCast.ipynb b/code/2_Physiology_or_Toxicity_ToxCast.ipynb → ...ks/2_Physiology_or_Toxicity_ToxCast.ipynb
diff --git a/code/3_qm9.ipynb → notebooks/3_qm9.ipynb b/code/3_qm9.ipynb → notebooks/3_qm9.ipynb
diff --git a/...sualization_of_Learning_aromaticity.ipynb → ...sualization_of_Learning_aromaticity.ipynb b/...sualization_of_Learning_aromaticity.ipynb → ...sualization_of_Learning_aromaticity.ipynb
diff --git a/...en_environment_and_water_solubility.ipynb → ...en_environment_and_water_solubility.ipynb b/...en_environment_and_water_solubility.ipynb → ...en_environment_and_water_solubility.ipynb
diff --git a/code/LICENSE → notebooks/LICENSE b/code/LICENSE → notebooks/LICENSE
diff --git a/code/fpscores.pkl.gz → notebooks/fpscores.pkl.gz b/code/fpscores.pkl.gz → notebooks/fpscores.pkl.gz
diff --git a/code/hyper_parameter_search_Solubility.py → ...ooks/hyper_parameter_search_Solubility.py b/code/hyper_parameter_search_Solubility.py → ...ooks/hyper_parameter_search_Solubility.py
diff --git a/code/run → notebooks/run b/code/run → notebooks/run
diff --git a/code/sascorer.py → notebooks/sascorer.py b/code/sascorer.py → notebooks/sascorer.py
diff --git a/code/saved_models/placeholder → notebooks/saved_models/placeholder b/code/saved_models/placeholder → notebooks/saved_models/placeholder
diff --git a/requirements.txt b/requirements.txt
@@ -0,0 +1,4 @@
+torch==1.0.1.post2
+rdkit==2019.03.1
+numpy==1.16.2
+matplotlib==3.0.3
diff --git a/setup.py b/setup.py
@@ -0,0 +1,13 @@
+from setuptools import find_packages, setup
+
+
+setup(
+    name='AttentiveFP',
+    version='0.0.1',
+    author='Erik Xiong',
+    description='Molecular representation using graph attention mechanism',
+    url='https://github.com/suleymanov/AttentiveFP',
+    license='MIT',
+    packages=find_packages(),
+    keywords=['chemistry', 'deep learning', 'graph attention']
+)